Richard Cole

A survey of formal concept analysis support for software engineering activities

Formal Concept Analysis (FCA) has typically been applied in the field of software engineering to support software maintenance and object-oriented class identification tasks. This paper presents a broader overview by describing and classifying academic papers that report the application of FCA to software engineering. The papers are classified using a framework based on the activities defined in the ISO12207 Software Engineering standard. Two alternate classification schemes based on the programming language under analysis and target application size are also discussed. In addition, the authors work to support agile methods and formal specification via FCA is introduced.

Document retrieval for e-mail search and discovery using formal concept analysis

This paper discusses a document discovery tool based on Conceptual Clustering by Formal Concept Analysis. The program allows users to navigate e-mail using a visual lattice metaphor rather than a tree. It implements a virtual file structure over e-mail where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in e-mail discovery. The system described provides more flexibility in retrieving stored e-mails than what is normally available ine-mail clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems and aid knowledge discovery in document collections.

A bare bones approach to literature-based discovery: an analysis of the raynaud's/fish-oil and migraine-magnesium discoveries in semantic space

Literature discovery can be characterized as a goal directed search for previously unknown implicit knowledge captured within a collection of scientific articles. Swansons serendipitous discovery of a treatment for Raynauds disease by dietary fish-oil while browsing Medline, an online collection of biomedical literature, exemplifies such a discovery. By means of a series of experiments, the impact of stop words, various weighting schemes, discovery mechanisms, and contextual reduction are studied in relation to replicating the Raynaud/fish-oil and migraine-magnesium discoveries by operational means. Two aspects of discovery were brought under focus: (i) the discovery of intermediate, or B –terms, and (ii) the discovery of indirect A – C connections via the B–terms. A semantic space representation of the underlying corpus is computed and discoveries automated by computing associations between words in both higher and contextually reduced spaces. It was found that the discovery of B–terms and A – C connections can be achieved to an encouraging degree with a standard stop word list. In addition, no single weighting scheme seems to suffice. Log-likelihood appears to be potentially effective for leading to the discovery of B–terms, whereas both odds ratio and simple co-occurrence frequencies both facilitate the discovery of A – C connections. With regard to discovery mechanism, both semantic similarity (via cosine) and information flow computation seem promising for computing A – C connections, but more research is needed to understand their relative strengths and weaknesses. Discovery in a contextually reduced semantic space revealed mixed results.

Towards Operational Abduction from a Cognitive Perspective

Diminishing awareness is a consequence of the information explosion: disciplines are becoming increasingly specialized; individuals and groups are becoming ever more insular. This article considers how awareness can be enhanced via operational abductive systems. The goal is to generate and justify suggestions which can span disparate islands of knowledge. Knowledge representation is motivated from a cognitive perspective. Words and concepts are represented as vectors in a high dimensional semantic space automatically derived from a text corpus. Various mechanisms will be presented for computing suggestions from semantic space: information flow, semantic similarity, pre-inductive generalization. The overall goal of this article is to introduce semantic space to the model-based reasoning and abduction community and to illustrate its potential for principled, operational abduction by semi-automatically replicating the Swanson Raynaud/fish oil discovery in medical text.

Structured Ontology and Information Retrieval for Email Search and Discovery

This paper discusses an document discovery tool based on formal concept analysis. The program allows users to navigate email using a visual lattice metaphor rather than a tree. It implements a virtual file structure over email where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in email discovery. The system described provides more flexibility in retrieving stored emails than what is normally available in email clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems.

Browsing Semi-Structured Texts on the Web Using Formal Concept Analysis

Browsing unstructured Web texts using Formal Concept Analysis (FCA) confronts two problems. Firstly, online Web data is sometimes unstructured and any FCA system must include additional mechanisms to discover the structure of input sources. Secondly, many online collections are large and dynamic, so a Web robot must be used to automatically extract data when it is required. These issues are addressed in this chapter, which reports a case study involving the construction of a Web-based FCA system used for browsing classified advertisements for real estate properties1. Real estate advertisements were chosen because they represent a typical semi-structured information source accessible on the Web. Furthermore, data is relevant only for a short period of time. Moreover, the analysis of real estate data is a classic example used in introductory courses on FCA. However, unlike the classic FCA real estate example, whose input is a structured relational database, we mine Web-based texts for their implicit structure. The issues encountered when mining these texts, and their subsequent presentation to the FCA system, are examined in this chapter. Our method uses a handcrafted parser for extracting structured information from the real estate advertisements, which are then browsed via a Web-based front-end employing rudimentary FCA system features. The user is able to quickly determine the trade-offs between different attributes of real estate properties and to alter the constraints of the search in order to locate good candidate properties. Interaction with the system is characterized as a mixed initiative process in which the user guides the computer in the satisfaction of constraints. These constraints are not specified apriori, but rather drawn from the data exploration process. Further, the chapter shows how the Conceptual Email Manager, a prototype FCA text information retrieval tool, can be adapted to the problem.

Automated layout of small lattices using layer diagrams

Good quality concept lattice drawings are required to effectively communicate logical structure in Formal Concept Analysis. Data analysis frameworks such as the Toscana System use manually arranged concept lattices to avoid the problem of automatically producing high quality lattices. This limits Toscana systems to a finite number of concept lattices that have been prepared a priori. To extend the use of formal concept analysis, automated techniques are required that can produce high quality concept lattice drawings on demand. This paper proposes and evaluates an adaption of layer diagrams to improve automated lattice drawing.

Navigation spaces for the conceptual analysis of software structure

Information technology of today is often concerned with information that is not only large in quantity but also complex in structure. Understanding this structure is important in many domains – many quantitative approaches such as data mining have been proposed to address this issue. This paper presents a conceptual approach based on Formal Concept Analysis. Using software source code as an example of a complex structure we present a framework for conceptually analysing relational structures. In our framework, a browsable space of sub-contexts is automatically derived from a database of relations augmented by a rule engine and schema information. Operations are provided for the user to navigate between sub-contexts. We demonstrate how the use of these operations can lead to quick identification of an area of software source code that establishes an unecessary dependency between software parts.

Agreement contexts in formal concept analysis

This paper describes a technique in which some columns of an n-ary relation are interpreted as defining a situation. For example when considering movies, critics and reviews we talk about the situation when the critic is a particular critic. We then construct a formal context called an agreement context designed to show that which is in common between the situations. We elaborate this idea in two ways: (i) by combining different types of situation; and (ii) using conceptual scales to introduce intervals of agreement.